Childhood maltreatment and educational outcomes: evidence from South Africa.

HEALTH ECONOMICS

Health Econ. 24: 876–894 (2015) Published online 29 May 2014 in Wiley Online Library (wileyonlinelibrary.com). DOI: 10.1002/hec.3065

CHILDHOOD MALTREATMENT AND EDUCATIONAL OUTCOMES: EVIDENCE FROM SOUTH AFRICA DUNCAN PIETERSE* National Treasury, Pretoria, South Africa

ABSTRACT Many South African children experience maltreatment, but we know little about the effects on long-term child development. Using the only representative dataset that includes a module on childhood maltreatment for a metropolitan city in South Africa, we explore the association between different measures of childhood maltreatment and two educational outcomes (numeracy test scores and dropout). Our study provides an estimate of the association between childhood maltreatment and educational outcomes in a developing country where maltreatment is high. We control for potential confounders using a range of statistical techniques and add several robustness checks to evaluate the strength of our findings. Our results indicate that children who are maltreated suffer large adverse consequences in terms of their numeracy test scores and probability of dropout and that the estimated effects of maltreatment are larger and more consistent for the most severe type of maltreatment. Copyright © 2014 John Wiley & Sons, Ltd. Received 1 May 2013; Revised 7 April 2014; Accepted 29 April 2014 KEY WORDS:

childhood maltreatment; education

1. INTRODUCTION One in two young South Africans indicate that family members often lose their tempers with each other (Leoschut, 2009), and one in four indicate that they are often hit at home as punishment for their wrongdoings (Leoschut and Burton, 2006). Two recent studies (Walker et al., 2007; Walker et al., 2011) identify exposure to violence as a major risk factor for long-term child development in developing countries. Using two distinct measures of educational outcomes (numeracy test scores and dropout), we explore the link between childhood maltreatment perpetrated by adults inside the home and educational outcomes. Evidence of a link between childhood maltreatment and educational outcomes is important for three reasons. First, to the extent that childhood maltreatment causes negative consequences for educational outcomes, it inhibits the production and non-production benefits of education and reduces the external benefits associated with a good education system. Because a key non-production benefit of education is a positive impact on health production (Lochner, 2011), to the extent that childhood maltreatment inhibits education, it will have a negative impact on health. Second, if childhood maltreatment causes an individual to drop out of school, it will also affect the efficiency of the education system1 as well as labour productivity. Third, if poor children are disproportionately maltreated, an association between childhood maltreatment and adverse educational outcomes may explain the persistence in inequality seen in the South African literature (Im et al., 2012).

*Correspondence to: National Treasury, 240 Madiba Street, Pretoria, South Africa. E-mail: [email protected] 1

For example, if the objective is to get a certain number of students through high school, then dropouts raise the cost of achieving that goal. In addition, large numbers of dropouts may distort normal instruction, raising the costs of schools (Hanushek et al., 2006).

Copyright © 2014 John Wiley & Sons, Ltd.

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Our empirical strategy is unable to identify the causal effect of childhood maltreatment on educational outcomes. Using an experimental design is not an option, and we lack a policy intervention (or other suitable mechanism) that creates credible exogenous variation in childhood maltreatment. We control for potential confounding effects using a variety of statistical techniques and point out the limitations of these methods in great detail. The consistency of our results across various specifications and estimation methods as well as the established pathways we discuss below provide a strong argument that we may be uncovering a causal effect. We contribute to the literature on the childhood origins of human skill formation and provide empirical evidence for recent theoretical work in this area. Thus far, the literature has focused on the relationship between educational outcomes and birth weight (Black et al., 2007), birth order (Hanushek, 1992; Black et al., 2011), height (Case and Paxson, 2008), early nutrition (Stein et al., 2005) and malaria during childhood (Venkataramani, 2012). We make three key contributions to the existing empirical literature: (i) we explore the relationship between adverse childhood experiences and educational outcomes in a developing country context; (ii) we use two measures of educational outcomes (numeracy test scores and dropout) and four measures of childhood maltreatment based on questions administered to all respondents (rather than focusing only on diagnosed cases), and we add an aggregate measure to reflect severity of maltreatment and explore the role of co-morbidities in the home during childhood (other adverse childhood experiences); and (iii) we add neighbourhood and sibling fixed effects to control for other confounding factors.

2. BACKGROUND Evidence from neuroscience indicates that because childhood is a period of heightened sensitivity to positive and negative influences, adverse experiences or stress during early childhood can cause permanent changes to brain architecture and gene expression in a manner that influences the formation of cognitive and non-cognitive skills (Knudsen et al., 2006). Cognitive skills refer to intelligence (Neisser et al., 1996), and non-cognitive skills include patience, self-control, temperament, motivation and time preference (Almlund et al., 2011). Evidence from the epidemiology literature indicates that parents are the active ingredients of environmental influence during childhood (Shonkoff and Phillips, 2000). Taken together, this suggests that childhood maltreatment may lead to adverse consequences for skill formation. The implications of these findings for educational outcomes are formalised by the human skill formation model, which is concerned with how skills are formed over the life cycle of an individual. The model emphasises the role of parents in shaping both cognitive and non-cognitive skills through genetic endowments and pre-natal and post-natal environments (Cunha et al., 2006; Cunha and Heckman, 2007; Cunha et al., 2010). Heckman (2000) and Heckman et al. (2006) show that both cognitive and non-cognitive skills matter for academic performance and educational attainment (what we broadly refer to as educational outcomes). A similar (less formal) theoretical model, entitled the life-course perspective, emphasises the role of family in shaping the intellectual and social development of children and suggests that childhood experiences shape the behaviour and educational performance of adults (Furstenberg et al., 1989). In the human skill formation model, each agent possesses a vector of cognitive and non-cognitive skills at each stage, which are used with different weights in different tasks. The process by which these cognitive and non-cognitive skills are produced is governed by a multi-stage technology, where all skills produced are a function of the inputs or investments at that stage. Cunha and Heckman (2007) highlight two important features of the multi-stage technology: self-productivity and dynamic complementarity. Self-productivity has two elements: (i) skills produced during one stage persist into future periods and (ii) non-cognitive skills may interact with cognitive skills to produce better outcomes (e.g. self-control and motivation may promote more vigorous acquisition of cognitive skills). Wolfe and Johnson (1995) and Duckworth and Seligman (2005) show the importance of non-cognitive skills for schooling performance, and Almlund et al. (2011) provide detailed evidence of how cognitive and non-cognitive skills interact to influence educational outcomes. Dynamic complementarity refers to the idea that skills produced during one stage raise the productivity of investment at Copyright © 2014 John Wiley & Sons, Ltd.

Health Econ. 24: 876–894 (2015) DOI: 10.1002/hec

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subsequent stages (in other words, investments in one period are more productive when there is a high level of capability in an earlier period). Childhood maltreatment is associated with anxiety, depression, attention-deficit or hyperactivity disorder, and aggressive behaviour (Famularo et al., 1992) as well as impaired impulse control and low self-regulation (Putnam, 2003). Studies from South Africa find an association between childhood maltreatment and post-traumatic stress disorder (Seedat et al., 2000), adverse effects on attention (Barbarin et al., 2001), and symptoms for depression and anxiety (Ward et al., 2001). Given these adverse consequences of childhood maltreatment, it seems likely that maltreatment affects the formation of non-cognitive skills such as patience, self-control, temperament and motivation. To the extent that childhood maltreatment affects the formation of non-cognitive skills, it may affect educational outcomes through a direct impact on non-cognitive skills and may also have an indirect impact through the importance of non-cognitive skills for the acquisition of cognitive skills. Economists have traditionally focused on two aspects of the economics of violence: (i) the determinants of the decision to perpetrate violence (following from the pioneering theoretical work by Becker (1968); refer to Freeman, 1999, for an empirical overview) and (ii) the adverse welfare effects of violence (Soares, 2006). The literature on the relationship between exposure to violence and educational outcomes is limited to studies on the effect of community or school violence (Grogger, 1997; Aizer, 2008; Sharkey, 2010). These studies do not evaluate the effect of childhood maltreatment and focus on narrow measurements of educational outcomes. There is some evidence on the effect of childhood maltreatment on educational outcomes from Latin America, where many countries have similar levels of violence to South Africa (Morrison and Orlando, 1999; Knaul and Ramirez, 2005).

3. DATA The Cape Area Panel Study (CAPS) is a longitudinal study of the lives of young people in Cape Town, South Africa. Wave 1 collected interviews from 4747 randomly selected young people aged 14–22 years in August– December, 2002. Household, school, work, childbearing and sexual behaviour data were collected for each young adult in the sample. For a complete discussion of the sample design, including selection of clusters, households and young adults, refer to Lam et al. (2006). We use weighted and unweighted CAPS data: the CAPS sampling weights create a weighted distribution of 14–22 year-olds by population group that is within one percentage point of the population group distribution in Cape Town in the 1996 census (Lam et al., 2008). Table I reports the unweighted and weighted descriptive statistics for Wave 1 of CAPS.2 We refer to the unweighted statistics whenever means are discussed. In Wave 1, there are slightly more women than men, the mean age is 18 years, 31% of young adults live in female-headed households, and 21% speak English at home. The household size as experienced by the typical young adult is 5.4 persons. The dummy for English as a home language is a proxy for class or social status under the assumption that, for coloured and black young adults in particular, speaking English at home is potentially an indication of a privileged background. There is evidence of a strong positive association between English as a home language and positive educational and labour market outcomes (Cornwell and Inder, 2007). The mean years of schooling of the respondents’ mothers is 9 years, and the mother’s education measure is missing for 11% of the respondents. Our measures of childhood maltreatment are based on questions asked to young people about violence perpetrated against them inside the home by their parents during childhood. There are four individual measures of childhood maltreatment and one aggregate score. Aggregate scores are important if we suspect that multiple adverse childhood experiences are associated with increases in the probability of adverse outcomes. In the case of childhood maltreatment, there are four biases we need to be aware of. First, reporting bias exists if, for example, girls are more willing to report childhood maltreatment compared with boys. On the basis of simple 2

The weighted descriptive statistics show that Cape Town has three predominant population groups (1996 Census percentages in brackets): coloured (58%), black (28%) and white (19%). The term coloured was originally used during apartheid to distinguish mixed-race South Africans from the European settlers and Bantu-speaking (black) people who settled the rest of present-day South Africa.



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Table I. Descriptive statistics: individual and family characteristics, Wave 1 Mean (sample) Individual characteristics Male Coloured Black White Age (years)

Mean (weighted)

Standard deviation

Sample size

0.45 0.42 0.45 0.13 17.88

0.48 0.53 0.28 0.19 17.92

0.5 0.49 0.5 0.33 2.48

4746 4746 4746 4746 4746

Household characteristics and family background Household size 5.43 Female-headed household 0.31 Home language English 0.21 Per capita income (Rands) 1094.71 Grant 0.03 Mother’s education (years) 8.85 Mother’s education missing 0.11

5.31 0.29 0.29 1455.73 0.03 9.33 0.10

2.51 0.46 0.40 1632.72 0.16 3.32 0.31

4746 4746 4746 4746 4712 4228 4746

Population means are weighted with weightyr (Wave 1). Refer to the Data Appendix for variable descriptions.

bivariate regressions, there is evidence that girls in our sample are more likely to report childhood maltreatment although we are unable to determine whether this is reporting bias or an indication of a real gender difference in maltreatment. Second, recall bias exists if, for example, older young adults tend to forget childhood maltreatment. We use simple bivariate regressions to investigate whether older young adults are less likely to report childhood maltreatment and find no evidence that this is the case. Third, selection bias exists if the young adults that choose not to answer the questions used to construct these dummies have systematically different experiences to those who do: refusal (non-response) rates for the measures of childhood maltreatment are always less than 1%, so this is unlikely to be a problem in our sample. Fourth, our measures of childhood maltreatment are based on self-reports, which are not verified independently. The measures used are based on standard questions used to elicit information on adverse childhood experiences and others have shown that childhood maltreatment data is valid provided it is collected properly (Dembo et al., 1991; Allen et al., 1994). In Table II, a higher proportion of coloured youth have been hit hard, pushed and put down by adults (i.e. being undermined psychologically) compared with other groups. A small number of white youth in the sample have been Table II. Childhood maltreatment by race and gender, Wave 1 (unweighted) Race

Hit hard Pushed Afraid of being hurt Put down by adults Childhood maltreatment

Gender

Black

Coloured

White

Male

Female

0.05 2094 0.17 2141 0.22 2144 0.32 2143 0.19 2139

0.08 1909 0.20 1992 0.17 2000 0.36 1994 0.20 1985

0.02 570 0.09 594 0.08 595 0.23 594 0.10 590

0.05 2055 0.16 2131 0.17 2134 0.31 2130 0.17 2122

0.06 2518 0.18 2596 0.19 2605 0.35 2601 0.20 2592

Number of observations in italics. The questions used to elicit childhood maltreatment responses asked young adults to reflect on their family life until age 14 years and always asked whether an adult, parent or stepparent living in their home was the perpetrator. Young adults were asked to reflect on childhood maltreatment using a five-point scale: 1 (never), 2 (once or twice), 3 (sometimes), 4 (often) and 5 (very often). The four dummies were constructed as follows: 1 if the young adult indicated sometimes, often or very often, and 0 if the young adult indicated never or once/twice. Childhood maltreatment is a sum of the maltreatment dummies divided by 4 (we therefore assign equal weight to each maltreatment category) and is calculated to return missing if any of the dummies for the four types of childhood maltreatment is missing, hence the lower sample size for this measure. Copyright © 2014 John Wiley & Sons, Ltd.


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hit hard (around 2%), compared with 5% for black youth and 8% for coloured youth. A substantially greater proportion of the youth have been pushed: 9% of white youth, 17% of black youth and 20% of coloured youth. A fear of being hurt is much greater among black youth (22%), compared with coloured (17%) and white (8%) youth. The score of maltreatment is highest for coloured and black young adults (0.2 and 0.19) and much lower for white young adults (0.1). These statistics suggest that childhood maltreatment is highest among coloured youth and a higher proportion of women have experienced maltreatment compared with men. This may reflect reporting differences between men and women: the emotional nature of fear of violence creates measurement problems if men under-acknowledge fear compared with women (Lemanski, 2006). Our findings are consistent with national trends on family violence: more coloured youth (33%) experience family violence, followed by black youth (22%), and more girls (29%) are hit at home as punishment for their wrongdoings compared with boys (26%) (Leoschut and Burton, 2006). Findings from South Africa’s National Youth Lifestyle Study (Leoschut, 2009) indicate that physical assaults within the home often include the use of weapons (32%) such as knives (44%), sticks (18%), and axes, pangas or bush knives (14%). In a study of the nature of injuries suffered by maltreatment victims at a children’s hospital in Cape Town, Naidoo (2000) found that the perpetrators were most often the father or stepfather of the child (36%), the mother’s boyfriend (20%) or the mother (12%); the child victims suffered serious injuries in 65% of the cases, with 49% being hospitalised, and the face was the most often injured area of the body (59%). In a study of non-accidental head injury over a 3-year period at the same children’s hospital in Cape Town, Fieggen et al. (2004) found that in 47% of cases, the injured child was not the intended target of the assailant; in the vast majority of cases, the perpetrator was male, and the intended adult victim was female. Wave 1 of CAPS contains 2125 ‘single’ young adults, 1808 individuals that are co-resident in pairs and 813 individuals that are co-resident in trios; we create a cluster household sample using co-resident pairs and co-resident trios (2621 individuals). Wave 1 of CAPS also contains 1211 sibling pairs and 341 sibling trios; we create a cluster sibling sample using sibling pairs and sibling trios (1552 individuals). We distinguish between co-resident and sibling pairs and trios because CAPS contains many young adults that are non-sibling co-resident pairs and trios (e.g. nephews/nieces, cousins, spouses, friends and uncles/aunts). The large difference between 2125 (household sample) and 1552 (sibling sample) is because when we drop a non-sibling young adult that is part of a co-resident pair, we also drop the remaining young adult (because the latter no longer has a true sibling in the home). In the empirical section, we exploit variation between young adults in the same household and young adults who are siblings to control for constant household effects and family background through household and sibling fixed-effect regressions. Variation between siblings could be due to the following: (i) observed individual heterogeneity (differences in gender or age); (ii) unobserved individual heterogeneity (e.g. delinquency, individual discount rates or genetic factors); or (iii) unobserved household heterogeneity (differences between siblings due to unobserved transitory family shocks that affect these measures for one sibling and not the other). Furthermore, variation between household members could be related to many of the possible differences in family background (e.g. between a young adult who is a biological child in the household compared with a young adult who joined the household from another family). The sibling sample means and standard deviations shown in Table III are very similar to the full sample statistics. Variation in these measures is always lower within a household than between households. Variation within households in the household sample is always higher than variation within households in the sibling sample because siblings have more in common than co-resident young adults. Siblings report differences in childhood maltreatment for all measures: the highest differences in maltreatment within households is reported for youth that were put down by adults, followed by those that were pushed and experienced a fear of being hurt. We expect differences in maltreatment between siblings – this can be linked to age, gender and role within the family or parental favouritism. In Table IV, we show the two educational outcome measures in this study: (i) scores from numeracy tests administered to all young adults during Wave 1 and (ii) educational attainment (dropout). The numeracy test scores are standardised to zero mean and unit variance in the original dataset and are used unchanged here. Each Copyright © 2014 John Wiley & Sons, Ltd.


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Table III. Sibling sample statistics: childhood maltreatment, Wave 1 (unweighted)

Hit hard Pushed Afraid hurt Adult put down Childhood maltreatment

Mean

Standard deviation

Number of observations

Standard deviation (between households)

Standard deviation (within households)

0.05 0.18 0.17 0.32 0.18

0.23 0.38 0.38 0.47 0.27

1548 1544 1551 1549 1541

0.17 0.3 0.31 0.38 0.23

0.15 0.23 0.22 0.27 0.15

Number of observations is the number of meaningful responses: the number of young adults for which the measure is one or zero in the sibling sample.

Table IV. Educational outcomes, CAPS Panel (unweighted) Race Variable Numeracy score Dropout

Gender

Sample

Black

Coloured

White

Male

Female

0 (1) 4691 0.45 (0.5) 3599

0.44 (0.77) 2120 0.53 (0.5) 1514

0.07 (0.91) 1981 0.49 (0.5) 1636

1.34 (0.69) 590 0.03 (0.17) 447

0.05 (1.04) 2115 0.47 (0.5) 1588

0.04 (0.97) 2576 0.42 (0.49) 2011

Standard deviations in brackets and number of observations in italics. The test scores are from Wave 1 and the dropout measure is from the full five-wave panel.

respondent in Wave 1 completed the same self-administered numeracy test; the test could be taken in either English or Afrikaans (there was no Xhosa version, the home language of most black respondents). Test scores exhibit striking racial differences: the performance of black youth is about two standard deviations lower than their white counterparts, and the performance of female youth is lower than male performance. This is consistent with findings from the USA that boys tend to outperform girls on numeracy tests (Downey and Vogt Yuan, 2005) and recent evidence from the USA of a gender gap in mathematics in favour of boys (Fryer and Levitt, 2010). There is an important caveat to keep in mind when interpreting the dropout measure: attrition bias exists if the young adults who drop out after exiting CAPS are systematically different along our measures of interest compared with those who remain in the sample. The dropout measure shows clear differences across race and gender: black youth have the highest dropout rate (53%), followed by coloured (49%) and white (3%) youth, and dropout rates are higher for men. Recent national data confirm these features of the South African education system: (i) women have higher pass rates than men in every grade of high school; (ii) dropout rates are higher for men than for women; and (iii) about 40% of the students who were in grade 11 in 2008 dropped out of the schooling system without completing the final year of high school by 2010 (Branson et al., 2012). The racial disparity reported for all educational outcomes is consistent with extreme differences in school quality along racial lines before the end of apartheid (Case and Deaton, 1999) and after (Yamauchi, 2005). Table V reports sibling sample statistics for educational outcomes. Numeracy test scores are higher in the sibling sample, and the sibling sample means for dropout are very similar to the full sample means discussed earlier. In all cases, variation in the educational outcome measures is lower within a household than between households.

4. EMPIRICAL STRATEGY Various factors have been associated with the educational outcomes of young people: (i) welfare (Cunha et al., 2006); (ii) lack of one loving and consistent adult (Shonkoff and Phillips, 2000); (iii) socio-economic status (Currie, 2009); (iv) household size (Rosenzweig and Wolpin, 1980; Behrman et al., 1982; Black et al., Copyright © 2014 John Wiley & Sons, Ltd.


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Table V. Sibling sample statistics: educational outcomes, CAPS Panel (unweighted)

Numeracy test scores Dropout

Mean

Standard deviation

Number of observations

Standard deviation (between households)

Standard deviation (within households)

0.66 0.44

1.03 0.5

1540 1249

0.94 0.44

0.42 0.26

Number of observations is the number of meaningful responses: the number of young adults for which the measure is one or zero in the sibling sample.

2005; Rosenzweig and Zhang, 2009); and (v) school quality (Dearden et al., 2002). The extent to which childhood maltreatment affects educational outcomes may also depend on the following: (i) individual characteristics (age, gender, individual personality or resilience and role within the family); (ii) the nature of childhood maltreatment (type, timing, frequency and length); and (iii) the presence of support systems (such as other family or school environment). Using CAPS allows us to control for a rich set of factors that may be correlated with both childhood maltreatment and educational outcomes. A failure to control for these factors would cause bias in the estimated effect of childhood maltreatment on educational outcomes. The empirical strategy used here is closely related to the reduced-form literature on determinants of educational outcomes (Glewwe and Kremer, 2006; Björklund and Salvanes, 2011). In this literature, educational outcomes are determined by individual characteristics, family background and neighbourhood characteristics: Y i ¼ β0 þ β1 Ii þ β2 Fi þ β3 DN þ β4 V i þ ε

(1)

where Yi is a measure of educational outcomes (numeracy test scores or dropout), Ii is a vector of individual characteristics (such as age and race), Fi is a vector of family characteristics (such as home language and household income), DN is a vector of neighbourhood dummies and Vi is a measure of childhood maltreatment. The error term, ε, can be decomposed into an individual specific error term that reflects unobserved individual characteristics, a family-specific error term that reflects unobserved family background such as parental competence and a random error term. It is possible that collective efficacy, defined as social cohesion among neighbours combined with their willingness to intervene on behalf of the common good (Sampson et al., 1999), is linked to child maltreatment and educational outcomes in a neighbourhood.3 For example, in certain neighbourhoods, neighbours may be more inclined to report parents who perpetrate violence against their children (Heise and Garcia-Moreno, 2002). Other studies have found an association between the perpetration of violence and neighbourhood characteristics such as unemployment rates, income levels, income inequality, poverty (Fleisher, 1966; Ehrlich, 1973; Freeman, 1992), collective efficacy (Jain et al., 2010) and social interactions (Glaeser et al., 1996). We add a vector of neighbourhood dummies, DN, to control for neighbourhood characteristics that may be correlated to childhood maltreatment and educational outcomes. The neighbourhood fixed-effect regressions contain dummies for 50 neighbourhoods based on the police precincts in the CAPS dataset. We use Equation (1) to obtain β4, the association between childhood maltreatment and educational outcomes. The estimate of β4 will be biased in the following ways: (i) if young adults are selected into childhood maltreatment based on unobserved factors (omitted variables bias); (ii) if households sort themselves into neighbourhoods in a way that affects both childhood maltreatment and educational outcomes (selection bias); and (iii) if educational outcomes or childhood maltreatment is measured with error (measurement error bias).4 3

Each South African school has a school governing body (SGB) that governs the management of the school. The SGB consists of the school principal, parents, teachers, senior students and other community members. SGBs are able to raise additional funds (by deciding on higher fees or fundraising initiatives) that can be used to appoint SGB-paid teachers in addition to the government-paid teachers to reduce pupil– teacher ratios. 4 The estimate of β4 may be biased in other ways: (i) it is theoretically possible (but unlikely) that academic performance or educational attainment affect how young adults reflect on childhood maltreatment (simultaneity bias), and (ii) if the relationship between childhood maltreatment and educational outcomes is not linear and additive, a linear specification will be the incorrect functional form of the conditional expectation (misspecification bias). Copyright © 2014 John Wiley & Sons, Ltd.



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First, the estimate of β4 will be biased if young adults are selected into childhood maltreatment based on unobserved factors; in other words, unobserved factors influence both educational outcomes and childhood maltreatment. The unobserved factors that conceivably drive both childhood maltreatment and educational outcomes may be at the individual level (delinquency) or household level (parental competence). For example, if a child has a greater propensity for delinquent behaviour that results in poor educational outcomes and childhood maltreatment, then the estimate of β4 would be biased upwards unless selection into this behaviour is observed. Alternatively, if parents who maltreat their children also fail to provide the necessary financial or other support during high school, then unobserved parental competence would lead to an upward bias in the estimate of β4. Second, parents can influence the quality of a school (refer to school governing body discussion in an earlier footnote), and all parents can choose from more than one school inside or outside their neighbourhood or can choose to move to a neighbourhood with better quality schools. In this case, because parents can affect school quality, selection bias is possible if neighbourhood sorting takes place in a way that determines educational outcomes in high school and childhood maltreatment. Third, the estimate of β4 will be biased if educational outcomes or childhood maltreatment is measured with error. Measurement error in educational outcomes (Yi) would bias the estimate of β4 if it were correlated with childhood maltreatment. For example, if the educational outcomes of young adults who experienced childhood maltreatment are systematically mis-measured compared with those that were not. Random measurement error in childhood maltreatment would lead to attenuation bias if the estimate of β4 were reduced as a result of reporting error in childhood maltreatment (in other words, the true variation is reduced by the variation that is due to reporting bias). Producing causal estimates is challenging because childhood maltreatment cannot be reproduced in an experimental setting and is not randomly distributed across households in Cape Town. Economists have used several strategies to address the biases described earlier: (i) controls for observables; (ii) exploiting variation between areas by using area-level variables as instruments (Evans et al., 1992; Cutler and Glaeser, 1997; Card and Rothstein, 2007); (iii) individual, household, school and grade fixed effects (Meghir and Rivkin, 2011); and (iv) social experiments or quasi experimental data (Sacerdote, 2001; Zimmerman, 2003; Kling et al., 2007). Glewwe and Kremer (2006) provide a critical review of these identification strategies. To address unobserved differences between young adults due to constant family background, we conduct household and sibling fixed-effect regressions. In the household fixed-effect regressions, we add a household dummy, DH, to Equation (1) to control for constant and unmeasured household characteristics that may be correlated with both childhood maltreatment and educational outcomes. In the sibling fixed-effect regressions, we add a sibling dummy, DS, to Equation (1) to control for constant and unmeasured family background (such as parental competence) that may be correlated with both childhood maltreatment and educational outcomes. One would expect that any omitted household or family variable is constant between siblings, controlling for observables. In all cases, we expect the bias due to omitted family background to be positive and therefore smaller estimates in the sibling fixed-effect regressions compared with the baseline and neighbourhood fixed-effect estimates. The sibling fixed-effect regressions are unable to address the following: (i) differences between siblings due to unobserved transitory family shocks (e.g. to a parent’s employment status) that affect both childhood maltreatment and educational outcomes for one sibling and not the other and (ii) differences between siblings based on their (unobserved) individual characteristics that affect both childhood maltreatment and educational outcomes for one sibling and not the other.5 Differences in maltreatment between siblings may be due to genetic factors (e.g. that affects delinquency), differences in pre-natal and post-natal environments between siblings, or parental rearing differences due to gender, age or other effects. If individual differences are related to age or gender, then including dummies for these measures may account for some of these unobserved differences between siblings. Furthermore, household fixed-effect regressions are unable to account for

5

Sibling fixed effects are also unable to address simultaneity bias due to the school environment when one sibling experiences childhood maltreatment in a way that affects their educational outcomes. This takes places if a teacher can influence whether one sibling is maltreated and not another (e.g. by encouraging parents to practice corporal punishment with one delinquent child).



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Table VI. Estimates of the association between childhood maltreatment and numeracy test scores

Hit hard Observations R-squared Pushed Observations R-squared Afraid of being hurt Observations R-squared Put down by adults Observations R-squared

All controls, no FEs

All controls – neighbourhood FE (full sample)

All controls – neighbourhood FE (male)

All controls – neighbourhood FE (female)

Household FE with age and gender dummies

Sibling FE with age and gender dummies

0.18*** (0.05) 4678 0.42 0.07* (0.04) 4669 0.42 0.07* (0.03) 4681 0.41 0.07*** (0.03) 4673 0.41

0.17*** (0.05) 4678 0.43 0.06* (0.03) 4669 0.43 0.07* (0.03) 4681 0.43 0.07*** (0.03) 4673 0.43

0.14 (0.09) 2106 0.45 0.10** (0.04) 2105 0.45 0.13** (0.05) 2108 0.45 0.08** (0.03) 2104 0.45

0.17*** (0.06) 2572 0.43 0.04 (0.04) 2564 0.43 0.01 (0.04) 2573 0.42 0.06* (0.03) 2569 0.42

0.10* (0.06) 2589 0.02 0.04 (0.04) 2586 0.02 0.06 (0.05) 2592 0.02 0.09** (0.04) 2588 0.02

0.06 (0.07) 1536 0.04 0.01 (0.04) 1532 0.04 0.00 (0.05) 1539 0.04 0.09* (0.05) 1537 0.05

FE, fixed effect. Each coefficient comes from a separate regression. Robust standard errors in brackets. Standard errors clustered at the neighbourhood level. All control variables are from Wave 1. Regressions with all controls include the following: age, male, black, coloured, log_income_pc, home_lang_english, hh_size, female_hh, hh_ownbooks, mother_education and mother_educ_missing. ***p < 0.01, **p < 0.05, *p < 0.1.

differences between co-resident young adults linked to other differences in family background (e.g. between a young adult who is a biological child in the household compared with a young adult who joined the household from another family). We use ordinary least squares when the measure of educational outcomes is continuous (numeracy test scores) and a linear probability model (LPM) when the measure of educational outcomes is binary (dropout). The LPM is used for three reasons. First, the LPM estimates the main parameters of interest (Angrist and Pischke, 2008), and the estimated coefficients give the change in probability for the explanatory variables holding the other variables constant. Estimating average partial effects after logit and probit models is arguably less efficient than the direct estimation process, and in practice, the coefficients from an LPM are generally very close to average partial effects estimated with logit or probit models. Second, LPMs can be estimated in a straightforward way with fixed effects (Angrist, 2001). The ‘fixed-effects logit’ (or conditional logistic regression) is less straightforward, and there is no fixed effects version of the probit model. The random effects versions are unattractive because of the strong assumptions involved. Third, a number of related studies that examine the long-term consequences of child maltreatment (e.g. Currie and Tekin, 2012) have also used LPMs. Although LPMs provide reasonable estimates of the average partial effects, they do incur the cost that individual predictions may end up outside the [0,1] interval.

5. REGRESSION ANALYSIS All regressions discussed here use the unweighted CAPS dataset and report robust and cluster-corrected standard errors.6 To economise on space, we present the full set of coefficients for the aggregate measure of maltreatment only. We start with ordinary least square regressions of the relationship between childhood maltreatment and numeracy test scores; each coefficient comes from a separate regression. In column 1 of Table VI, 6

Weighted regressions and average marginal effects (from a logit regression for the binary dropout measure) are available from the authors upon request. The maltreatment estimates are the same as those reported here.




885

we add the full list of controls without any fixed effects. The coefficients on the controls are consistent with the literature and the descriptive statistics reviewed earlier: being black (one standard deviation) and coloured (three-quarters of a standard deviation) are associated with large reductions in numeracy test scores relative to the omitted category of white. Increases in income, having English as a home language and living in a household where someone owns more than five books are associated with increases in numeracy test scores. A 1-year increase in mother’s education is associated with a 0.05 increase in numeracy test scores. All measures of childhood maltreatment are significant. Being hit hard is associated with a 0.18 reduction in numeracy test scores. The mean numeracy test score for those young adults who were not hit hard is 0.02, so a mean reduction of 0.18 yields a numeracy test score of 0.16. Given that numeracy test scores range between 1.7 and 2.3 with unit variance, a reduction of 0.18 results in a decrease in numeracy test scores equivalent to about one-fifth of a standard deviation. A reduction of 0.18 in numeracy test scores seems small, but it is larger than the impact of mother’s education and household income on numeracy test scores. For all maltreatment measures, being maltreated moves the average young adult below the mean numeracy test score of zero. In column 2 of Table VI, we add dummies for 50 neighbourhoods in the CAPS dataset: living in Atlantis, Khayelitsha, Langa and Nyanga is associated with a 0.21–0.25 reduction in numeracy test scores. The childhood maltreatment coefficients remain largely unchanged. Khayelitsha, Langa and Nyanga are black townships in the Cape Flats where the unemployment rate ranges between 32 and 55% and where the legacy of the apartheid-era education system (e.g. poor teacher quality and high pupil–teacher ratios) have not been overcome. Many of the residents of these township areas are originally from the Eastern Cape where the apartheid-era education system was administered by separate black departments of education in the Ciskei and Transkei and where educational outcomes were especially poor – 24% of the young adults in CAPS indicate that they were born in the Eastern Cape. The literature suggests that girls and boys may respond differently to childhood maltreatment (Holt et al., 2008); the next two columns present neighbourhood fixed-effect estimates by gender. There is substantial variation in the estimated effect of maltreatment on the numeracy test scores of boys and girls. The large negative effect of being hit hard on numeracy test scores seems to be driven by the girls in the sample, and the effects of being pushed and a fear of being hurt are higher for boys. The estimated effect of being put down by adults on numeracy test scores is similar for boys and girls. We indicated before that the estimated effect of childhood maltreatment could include both the actual effect of maltreatment as well as the effect due to constant household characteristics and family background. We perform household and sibling fixed-effect regressions as a robustness check. The household and sibling fixed-effect regressions include age and gender dummies, so these regressions effectively control for household characteristics (in the case of household fixed effects), family background (in the case of sibling fixed effects) as well differences between co-residents and siblings due to age or gender. In the household fixed-effect regressions, age is always associated with a 0.03 increase in numeracy test scores, the gender dummy is never significant and the estimated effect of maltreatment decreases (with the exception of being put down by adults). The effect of being hit hard on numeracy test scores in the household fixed-effect regressions is 0.1 (one-tenth of a standard deviation). In the sibling fixed-effect regressions, the estimated effect of maltreatment decreases and the coefficients become insignificant (with the exception of being put down by adults). Because of smaller sample sizes in the household and sibling fixed-effect regressions and the resultant increase in standard errors, many of the coefficients have become insignificant. These household and sibling fixed-effect regressions are unable to account for differences between siblings who attend schools of different quality. As mentioned before, the estimated coefficients of maltreatment from the sibling fixed-effect regressions potentially still include the effect due to differences between siblings related delinquency and ability that affect both childhood maltreatment and educational outcomes. Second, we estimate LPM regressions of the relationship between childhood maltreatment and dropout in Table VII. Again, the coefficients on the controls are consistent with the literature reviewed earlier: being male is associated with a 0.07 increase in the probability of dropout compared with women and being coloured is Copyright © 2014 John Wiley & Sons, Ltd.


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Table VII. Estimates of association between childhood maltreatment and dropout

Hit hard Observations R-squared Pushed Observations R-squared Afraid of being hurt Observations R-squared Put down by adults Observations R-squared



All controls – neighbourhood FE (males only)

All controls – neighbourhood FE (females only)



0.15*** (0.03) 3586 0.25 0.15*** (0.05) 2033 0.04 0.08*** (0.02) 3589 0.25 0.02 (0.02) 3582 0.25

0.16*** (0.03) 3586 0.27 0.06*** (0.02) 3578 0.25 0.08*** (0.02) 3589 0.27 0.02 (0.02) 3582 0.26

0.14*** (0.05) 1580 0.25 0.08*** (0.02) 1579 0.25 0.09*** (0.03) 1582 0.26 0.04 (0.02) 1579 0.25

0.15*** (0.03) 2006 0.30 0.04 (0.02) 1999 0.30 0.08*** (0.03) 2007 0.30 0.02 (0.02) 2003 0.30

0.16*** (0.06) 2033 0.03 0.06*** (0.02) 3578 0.26 0.11** (0.05) 2035 0.03 0.01 (0.03) 2031 0.03

0.19** (0.09) 1245 0.06 0.06 (0.04) 1242 0.05 0.13** (0.06) 1248 0.06 0.02 (0.04) 1246 0.05

FE, fixed effect. Each coefficient comes from a separate regression. Robust standard errors in brackets. Standard errors clustered at the neighbourhood level. All control variables are from Wave 1. Regressions with all controls include the following: age, male, black, coloured, log_income_pc, home_lang_english, hh_size, female_hh, hh_ownbooks, mother_education and mother_educ_missing. ***p < 0.01, **p < 0.05, *p < 0.1.

associated with a 0.1 increase in the probability of dropout (compared to white, the omitted category). Increases in income, having English as a home language and living in a household where someone owns more than five books are associated with decreases in the probability of dropout, and a 1-year increase in mother’s education is associated with a 0.02 decrease in the probability of dropout. The estimated effect of childhood maltreatment on the probability of dropout is large and significant. Being hit hard or pushed increases the probability of dropout by 0.15. The mean dropout rate among those who were not hit hard is about 43%, so an increase of 15% results in a dropout rate among those who have been hit hard of almost 60%. The estimated effects of a fear of being hurt (0.08) and pushed (0.06) are smaller. The mean dropout rate among those who were not pushed or afraid of being hurt are 42%, so these estimated increases of 8 and 6%, increases the dropout rate for these categories of maltreatment to around 50%. There is no association between being put down by adults and the probability of dropout. This is in contrast to the numeracy test score findings, which indicate a large and consistent effect for being put down by adults. In column 2 of Table VII, we add neighbourhood dummies: living in Steenberg (0.21), Atlantis (0.15) and Nyanga (0.11) is associated with large increases in the probability of dropout. The childhood maltreatment coefficients remain largely unchanged, with the exception of being pushed, which reduces to 0.06. The next two columns present neighbourhood fixed-effect estimates by gender. The estimated effects of childhood maltreatment on the probability of dropout is very similar for boys and girls overall. The exception is being pushed during childhood, which has a much larger effect on the probability of dropout for boys compared with girls. Controlling for constant household characteristics through household fixed-effect regressions leaves the estimated effect of childhood maltreatment on the probability of dropout largely unchanged, and the standard errors are higher. The exception is a fear of being hurt, where the estimated effect increases to 0.11. The larger estimated effect in the co-resident sub-sample for a fear of being hurt suggests that the effects of this type of maltreatment may be higher in the co-resident sample. This makes sense if we expect co-resident young adults to be more likely to report a fear of being hurt inside the home compared with young adults who are singletons. In the household fixed-effect regressions, being male is always associated with a 0.08 increase in the Copyright © 2014 John Wiley & Sons, Ltd.



887

probability of dropout; this is in contrast to the numeracy test score regressions where the gender dummy was never significant. Controlling for constant family background through sibling fixed-effect regressions leaves the estimated effect of childhood maltreatment on the probability of dropout largely unchanged. However, the coefficients for being hit hard and a fear of being hurt are slightly higher, which suggests that these types of maltreatment may be higher in the sibling sample. This makes sense if we expect siblings to be more likely to report being hit hard and a fear of being hurt inside the home and is consistent with homes where parents practice corporal punishment with their children. Because we suspect that the severity of childhood maltreatment matters, in Table VIII, we estimate the effect of aggregate childhood maltreatment on numeracy test scores and the probability of dropout. The aggregate measure is associated with a decrease in numeracy test scores of about 0.14 and a 0.12 increase in the probability of dropout. Both effects are robust to the inclusion of household fixed effects. The effect is no longer significant in sibling fixed-effect regressions where numeracy test scores are the outcome variable and the size of the coefficient is smaller, which likely reflects the removal of upward bias due to constant family background. In sibling fixed-effect regressions where dropout is the outcome variable, the coefficient is larger and significant. There is evidence that the higher the number of risk factors a child encounters in early childhood, the lower the child’s educational attainment and the higher the level of internalising behaviour problems in adolescence (Gorman and Pollitt, 1996; Corapci et al., 2006). The presence of other risk factors during childhood (co-morbidities) may bias our estimates. To determine the presence of adverse childhood experiences, respondents were asked whether they had a problem drinker or alcoholic, drug user, mentally ill person or criminal in the home during childhood, and we included their answers in neighbourhood fixed-effect regressions (Table IX). Adding other adverse experiences during childhood reduces the size of the childhood maltreatment coefficients slightly and in one case (the effect of being pushed on numeracy test scores) leads to insignificance. In the numeracy test score regressions, having a criminal in the home during childhood is associated with a reduction in test scores of 0.08, and in the dropout regressions, having a problem drinker in the home during childhood is associated with an increase of 0.04 in the probability of dropout.

6. DISCUSSION Children who experience maltreatment suffer large adverse consequences in terms of their numeracy test scores and probability of dropout. These results are consistent for weighted and unweighted regressions, robust to the addition of co-morbidities, choice of estimation method (LPM and logit specifications) and sibling fixed effects and are valid for two different measures of educational outcomes. This suggests that the adverse consequences of child maltreatment are not simply artefacts of family background or household characteristics. On average, 6 in every 10 young adults who were hit hard do not complete high school compared with 4 in every 10 young adults among those who were not maltreated. This has significant implications for health production given the employment and earnings premiums from completing high school. In recent evidence, using nationally representative data, Branson et al. (2013) show that at age 25 years, the cohort of those who completed high school with the poorest labour market outcomes still earned 40% more and were 25% more likely to be employed than those who dropped out in the last 2 years of high school. There is substantial variation in the estimated effect of maltreatment on boys and girls. The summary statistics show that a higher proportion of women have experienced maltreatment, and their performance in numeracy tests is lower than men. The regression results indicate that the large negative association between being hit hard and numeracy test scores is driven by the women in the sample. It is clear that the type of childhood maltreatment matters: being hit hard is consistently associated with large adverse effects on educational outcomes. The other measures of childhood maltreatment are not always significant or robust to the inclusion of household or sibling fixed effects. The effects of maltreatment are large Copyright © 2014 John Wiley & Sons, Ltd.



0.05*** (0.00) 0.05 (0.04) 1.02*** (0.10) 0.76*** (0.08) 0.12*** (0.02) 0.33*** (0.04) 0.01 (0.01) 0.04 (0.04) 0.15*** (0.05) 0.05*** (0.01) 0.28*** (0.07) 0.15*** (0.05) 1.43*** (0.23) 4656 0.42 –

0.05*** (0.00) 0.05 (0.04) 0.89*** (0.12) 0.73*** (0.08) 0.12*** (0.02) 0.34*** (0.04) 0.01 (0.01) 0.03 (0.04) 0.15*** (0.04) 0.04*** (0.01) 0.24*** (0.06) 0.14*** (0.04) 1.27*** (0.24) 4656 0.43 –


0.15*** (0.05) 0.54*** (0.13) 2579 0.02 1173

0.03*** (0.01) 0.03 (0.05)


0.11 (0.08) 0.71*** (0.18) 1529 0.04 719

0.05*** (0.01) 0.00 (0.04)


FE, fixed effect. Robust standard errors in brackets. Standard errors clustered at the neighbourhood level. ***p < 0.01, **p < 0.05, *p < 0.1.

Observations R-squared Number of households

Constant

childhd_maltreatment

mother_educ_missing

mother_education

hh_ownbooks

female_hh

hh_size

home_lang_english

log_income_pc

coloured

black

male

age


Numeracy test scores All controls – neighbourhood FE (full sample) 0.02*** (0.00) 0.07*** (0.02) 0.06 (0.06) 0.11*** (0.04) 0.08*** (0.01) 0.16*** (0.03) 0.01* (0.00) 0.02 (0.04) 0.14*** (0.03) 0.02*** (0.00) 0.15*** (0.03) 0.12*** (0.02) 1.57*** (0.09) 3567 0.27 –

All controls, no FEs 0.02*** (0.00) 0.07*** (0.02) 0.01 (0.04) 0.13*** (0.03) 0.08*** (0.01) 0.15*** (0.03) 0.01** (0.00) 0.03 (0.04) 0.14*** (0.03) 0.02*** (0.00) 0.16*** (0.03) 0.11*** (0.02) 1.61*** (0.08) 3567 0.25 –

0.10* (0.06) 0.75*** (0.11) 2024 0.03 1079

0.02*** (0.01) 0.08*** (0.03)


Dropout

Table VIII. Estimates of association between aggregate childhood maltreatment and educational outcomes

0.14* (0.07) 0.85*** (0.12) 1239 0.05 670

0.03*** (0.01) 0.09*** (0.03)


888 D. PIETERSE



Observations R-squared

Constant

childhd_mental_ill

childhd_jail

childhd_drug_user

childhd_prb_drink

adult_put_down

afraid_hurt

pushed

hit_hard

mother_educ_missing

mother_education

hh_ownbooks

female_hh

hh_size

home_lang_english

log_income_pc

coloured

black

male

age

0.04 (0.03) 0.06 (0.04) 0.08* (0.05) 0.03 (0.05) 1.26*** (0.24) 4614 0.43

0.05*** (0.00) 0.05 (0.04) 0.90*** (0.12) 0.73*** (0.08) 0.12*** (0.02) 0.33*** (0.04) 0.01 (0.01) 0.03 (0.03) 0.16*** (0.04) 0.04*** (0.01) 0.24*** (0.06) 0.16*** (0.05)

0.04 (0.03) 0.05 (0.04) 0.08 (0.05) 0.03 (0.05) 1.26*** (0.23) 4607 0.43

0.06 (0.03)

0.05*** (0.00) 0.06 (0.04) 0.90*** (0.12) 0.73*** (0.08) 0.12*** (0.02) 0.33*** (0.04) 0.01 (0.01) 0.03 (0.03) 0.16*** (0.04) 0.04*** (0.01) 0.24*** (0.06)

0.04 (0.03) 0.06 (0.04) 0.09* (0.05) 0.03 (0.05) 1.27*** (0.23) 4616 0.43

0.06* (0.03)

0.05*** (0.00) 0.05 (0.04) 0.90*** (0.12) 0.73*** (0.08) 0.12*** (0.02) 0.33*** (0.04) 0.01 (0.01) 0.03 (0.03) 0.16*** (0.04) 0.04*** (0.01) 0.25*** (0.06)

Numeracy test scores

0.07*** (0.03) 0.04 (0.03) 0.06 (0.04) 0.09* (0.05) 0.04 (0.05) 1.26*** (0.23) 4608 0.43

0.05*** (0.00) 0.05 (0.04) 0.90*** (0.13) 0.73*** (0.08) 0.11*** (0.02) 0.33*** (0.04) 0.01 (0.01) 0.03 (0.03) 0.16*** (0.04) 0.04*** (0.01) 0.25*** (0.06)

0.04* (0.02) 0.05 (0.03) 0.03 (0.03) 0.03** (0.01) 1.55*** (0.09) 3543 0.27

0.02*** (0.00) 0.06*** (0.02) 0.06 (0.06) 0.11*** (0.04) 0.08*** (0.01) 0.16*** (0.03) 0.01* (0.00) 0.03 (0.04) 0.14*** (0.02) 0.02*** (0.00) 0.14*** (0.04) 0.15*** (0.02)

0.05* (0.02) 0.06* (0.03) 0.04 (0.03) 0.03** (0.01) 1.56*** (0.08) 3536 0.27

0.05*** (0.02)

0.02*** (0.00) 0.06*** (0.02) 0.06 (0.06) 0.11*** (0.04) 0.08*** (0.01) 0.16*** (0.03) 0.01** (0.00) 0.02 (0.04) 0.15*** (0.03) 0.02*** (0.00) 0.14*** (0.04)

Dropout

0.04* (0.02) 0.05 (0.03) 0.04 (0.03) 0.03** (0.01) 1.56*** (0.08) 3544 0.27

0.07*** (0.02)

0.02*** (0.00) 0.06*** (0.02) 0.07 (0.06) 0.10*** (0.04) 0.08*** (0.01) 0.16*** (0.03) 0.01** (0.00) 0.02 (0.04) 0.14*** (0.03) 0.02*** (0.00) 0.14*** (0.03)

Table IX. Estimates of association between childhood maltreatment and educational outcomes: adding co-morbidities

(Continues)

0.02 (0.02) 0.05** (0.02) 0.06 (0.03) 0.04 (0.03) 0.03** (0.02) 1.57*** (0.09) 3537 0.27

0.02*** (0.00) 0.06*** (0.02) 0.06 (0.06) 0.11*** (0.04) 0.08*** (0.01) 0.16*** (0.03) 0.01** (0.00) 0.03 (0.04) 0.15*** (0.03) 0.02*** (0.00) 0.14*** (0.03)


889


Copyright © 2014 John Wiley & Sons, Ltd. Y N N

Y N N

Y N N

FE, fixed effect. Robust standard errors in brackets. Standard errors clustered at the neighbourhood level. ***p < 0.01, **p < 0.05, *p < 0.1.

Neighbourhood FE Household FE Sibling FE

Numeracy test scores Y N N

Table IX. (Continued)

Y N N

Y N N

Dropout Y N N

Y N N

890 D. PIETERSE



891

relative to other factors that influence educational outcomes in South Africa. Using the aggregate measure of maltreatment, the effect on educational outcomes is always much smaller than the influence of race; however, the predictive power of maltreatment is larger than mother’s education and income. The intervention literature (Olds et al., 1997) suggests that a programme of prenatal and early childhood home visitation by nurses can reduce maltreatment by low-income, unmarried mothers for up to 15 years after the birth of the first child. Analysis by the Washington State Institute for Public Policy indicates that the dollar value of the net benefits of this programme is higher than other child welfare public policies. A similar programme is currently being tested in Cape Town, South Africa. In a follow-up to a randomised control trial that consisted of a home-based intervention by community health care workers to promote mothers’ engagement in sensitive and responsive interactions with their infants (Tomlinson et al., 2005), researchers are currently assessing child cognitive functioning and school attainment to determine whether the initial intervention had a long-term impact on skills formation. This and other assessments will help determine which interventions have the greatest potential to address the long-term adverse effect of maltreatment on educational outcomes in a high-risk developing-country context.

ACKNOWLEDGEMENTS

The author would like to thank Prof. Nicoli Nattrass and Prof. Martin Wittenberg at the University of Cape Town for comments and suggestions that substantially improved the quality of the paper. Financial assistance was received from the African Economic Research Consortium, National Research Foundation (South Africa) and the Centre for Social Science Research (University of Cape Town). A substantial part of this paper was completed while the author was a Graduate Program for Development fellow at the Watson Institute, Brown University.

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